We have developed algorithms for comparison and alignment of protein three dimensional structures. VAST (vector alignment search tool) identifies substructure similarities by comparing the types, connectivity, and relative orientations of SSE's (secondary structure elements). Surprising similarities are identified objectively, by considering the number and scores of superimposable SSE-pairs in the best alignment, and the number of alternative alignments sampled. An optimal residue-by-residue alignments are also identified objectively, as that with the most surprising combination of superposition residual and number of aligned residues. Work has focused in three areas. The first is construction of an automated incremental update system, to maintain an all-against-all database of the "structure neighbor" relationships among domain structures in the public database. The VAST neighbor database now contains nearly 30 million structural superpositions alignments. The second area is construction of an "on-the-fly" structure neighbor server, which is now in use by structural biologists. This server allows them to transmit confidential coordinate data for VAST comparison against the public structure database, to identify possible remote homologs and to map features from one family member to another. The third area of work this year has been a research project aimed at distinguishing structure neighbors that are related by descent from a common ancestral gene from those that are related by convergence to energetically preferred folding motifs. We have shown that homologs and "analogs", as they have been called, may be better distinguished by a test for the HCS (Homologous Core Structure), the substructure conserved among previously identified homologs, than by any measures proposed earlier. Research is in progress to fully automate HCS calculations, and to construct multiple structure alignments of homologous protein domains as clustered by HCS overlap.